Análisis 4: pEhExvsU2AF84
setwd("~/Documents/GitHub/Resultados/docs/PrimeroDiffExpAllResults/Clasificando/Abundances")
load('CheackPointOne.RData')Cuarta Clasificacion: pEhExvsU2AF84
head(pEhExvsU2AF84,10);## GenId CDC5_1 CDC5_2 CDC5_3 pEhEx_1 pEhEx_2 pEhEx_3
## 1 EHI_000130A 59.61996 70.37838 610.61850 140.16143 346.92628 381.749395
## 2 EHI_000140A 118.22941 206.99523 91.94642 282.69848 273.39300 30.952654
## 3 EHI_000240A 689.16628 871.44992 11090.15226 940.74452 1549.85546 1693.797993
## 4 EHI_000250A 426.43427 406.74563 1607.88347 260.13011 262.08018 525.621915
## 5 EHI_000260A 104.08230 109.70747 422.01047 87.30394 53.73586 40.123810
## 6 EHI_000280A 40.42031 35.18919 108.44962 43.94892 66.93415 49.294967
## 7 EHI_000290A 15.15762 19.66455 44.79441 14.84761 11.31281 64.771294
## 8 EHI_000300A 74.77757 94.18283 2.35760 106.90279 92.38798 25.220681
## 9 EHI_000410A 15.15762 15.52464 134.38322 22.56837 19.79742 52.160953
## 10 EHI_000430A 18.18914 23.80445 28.29120 20.78665 11.31281 8.597959nbreaks <- 10
data4 <- pEhExvsU2AF84; head(data4)## GenId CDC5_1 CDC5_2 CDC5_3 pEhEx_1 pEhEx_2 pEhEx_3
## 1 EHI_000130A 59.61996 70.37838 610.61850 140.16143 346.92628 381.74940
## 2 EHI_000140A 118.22941 206.99523 91.94642 282.69848 273.39300 30.95265
## 3 EHI_000240A 689.16628 871.44992 11090.15226 940.74452 1549.85546 1693.79799
## 4 EHI_000250A 426.43427 406.74563 1607.88347 260.13011 262.08018 525.62192
## 5 EHI_000260A 104.08230 109.70747 422.01047 87.30394 53.73586 40.12381
## 6 EHI_000280A 40.42031 35.18919 108.44962 43.94892 66.93415 49.29497Log-Normalización
sample1 <- data4$pEhEx_1; sample2 <- data4$pEhEx_2; sample3 <- data4$pEhEx_3;
samplevs1 <- data4$CDC5_1; samplevs2 <- data4$CDC5_2; samplevs3 <- data4$CDC5_3;
log2sample1 <- log2(sample1+1); log2sample2 <- log2(sample2+1)
log2sample3 <- log2(sample3+1); log2samplevsCDC51 <- log2(samplevs1+1)
log2samplevsCDC52 <- log2(samplevs2+1); log2samplevsCDC53 <- log2(samplevs3+1)
data4 <- cbind(data4, log2sample1,log2sample2,log2sample3,
log2samplevsCDC51,log2samplevsCDC52,log2samplevsCDC53)
head(data4)## GenId CDC5_1 CDC5_2 CDC5_3 pEhEx_1 pEhEx_2 pEhEx_3
## 1 EHI_000130A 59.61996 70.37838 610.61850 140.16143 346.92628 381.74940
## 2 EHI_000140A 118.22941 206.99523 91.94642 282.69848 273.39300 30.95265
## 3 EHI_000240A 689.16628 871.44992 11090.15226 940.74452 1549.85546 1693.79799
## 4 EHI_000250A 426.43427 406.74563 1607.88347 260.13011 262.08018 525.62192
## 5 EHI_000260A 104.08230 109.70747 422.01047 87.30394 53.73586 40.12381
## 6 EHI_000280A 40.42031 35.18919 108.44962 43.94892 66.93415 49.29497
## log2sample1 log2sample2 log2sample3 log2samplevsCDC51 log2samplevsCDC52
## 1 7.141202 8.442638 8.580256 5.921721 6.157415
## 2 8.148215 8.100100 4.997864 6.897596 7.700407
## 3 9.879192 10.598849 10.726898 9.430800 9.768929
## 4 8.028625 8.039359 9.040624 8.739559 8.671526
## 5 6.464406 5.774415 5.361902 6.715376 6.790609
## 6 5.490215 6.086065 5.652342 5.372266 5.177487
## log2samplevsCDC53
## 1 9.256488
## 2 6.538327
## 3 13.437122
## 4 10.651844
## 5 8.724550
## 6 6.774123save.image('CheckPointFour.RData')
setwd("~/Documents/GitHub/Resultados/docs/PrimeroDiffExpAllResults/Clasificando/Abundances")
#load('CheckPointTwo.RData')library(ggplot2);library(dplyr);library("fitdistrplus");##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union## Loading required package: MASS##
## Attaching package: 'MASS'## The following object is masked from 'package:dplyr':
##
## select## Loading required package: survivallibrary("MASS");library("survival")head(data4)## GenId CDC5_1 CDC5_2 CDC5_3 pEhEx_1 pEhEx_2 pEhEx_3
## 1 EHI_000130A 59.61996 70.37838 610.61850 140.16143 346.92628 381.74940
## 2 EHI_000140A 118.22941 206.99523 91.94642 282.69848 273.39300 30.95265
## 3 EHI_000240A 689.16628 871.44992 11090.15226 940.74452 1549.85546 1693.79799
## 4 EHI_000250A 426.43427 406.74563 1607.88347 260.13011 262.08018 525.62192
## 5 EHI_000260A 104.08230 109.70747 422.01047 87.30394 53.73586 40.12381
## 6 EHI_000280A 40.42031 35.18919 108.44962 43.94892 66.93415 49.29497
## log2sample1 log2sample2 log2sample3 log2samplevsCDC51 log2samplevsCDC52
## 1 7.141202 8.442638 8.580256 5.921721 6.157415
## 2 8.148215 8.100100 4.997864 6.897596 7.700407
## 3 9.879192 10.598849 10.726898 9.430800 9.768929
## 4 8.028625 8.039359 9.040624 8.739559 8.671526
## 5 6.464406 5.774415 5.361902 6.715376 6.790609
## 6 5.490215 6.086065 5.652342 5.372266 5.177487
## log2samplevsCDC53
## 1 9.256488
## 2 6.538327
## 3 13.437122
## 4 10.651844
## 5 8.724550
## 6 6.774123Muestra 1
log2sample1 <- data4$log2sample1; head(mean(log2sample1)); head(sd(log2sample1))## [1] 6.066239## [1] 2.837457head(log2sample1,5)## [1] 7.141202 8.148215 9.879192 8.028625 6.464406summary(data4)## GenId CDC5_1 CDC5_2 CDC5_3
## Length:4746 Min. : 0.00 Min. : 0.00 Min. : 0.0
## Class :character 1st Qu.: 15.16 1st Qu.: 14.49 1st Qu.: 14.1
## Mode :character Median : 41.43 Median : 42.43 Median : 49.5
## Mean : 1422.93 Mean : 1431.84 Mean : 3498.7
## 3rd Qu.: 168.75 3rd Qu.: 187.33 3rd Qu.: 237.5
## Max. :236529.55 Max. :222485.72 Max. :1942165.3
## pEhEx_1 pEhEx_2 pEhEx_3 log2sample1
## Min. : 0.00 Min. : 0.00 Min. : 0.0 Min. : 0.000
## 1st Qu.: 14.85 1st Qu.: 13.20 1st Qu.: 12.6 1st Qu.: 3.986
## Median : 42.17 Median : 42.42 Median : 44.7 Median : 5.432
## Mean : 1156.92 Mean : 1454.96 Mean : 1569.6 Mean : 6.066
## 3rd Qu.: 172.68 3rd Qu.: 191.38 3rd Qu.: 199.5 3rd Qu.: 7.440
## Max. :170957.75 Max. :224129.46 Max. :578317.1 Max. :17.383
## log2sample2 log2sample3 log2samplevsCDC51 log2samplevsCDC52
## Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 3.828 1st Qu.: 3.767 1st Qu.: 4.014 1st Qu.: 3.953
## Median : 5.440 Median : 5.514 Median : 5.407 Median : 5.441
## Mean : 6.019 Mean : 5.939 Mean : 6.074 Mean : 6.080
## 3rd Qu.: 7.588 3rd Qu.: 7.647 3rd Qu.: 7.407 3rd Qu.: 7.557
## Max. :17.774 Max. :19.142 Max. :17.852 Max. :17.763
## log2samplevsCDC53
## Min. : 0.000
## 1st Qu.: 3.921
## Median : 5.658
## Mean : 6.152
## 3rd Qu.: 7.898
## Max. :20.889ndata4 <- length(log2sample1)
hist(log2sample1, breaks = nbreaks, col= rainbow(25,0.3),
main = 'Log2 sample1')
meanlog2sample1 <- mean(log2sample1); head(meanlog2sample1)## [1] 6.066239StdDevlog2sample1 <- sd(log2sample1); head(StdDevlog2sample1)## [1] 2.837457Normlog2sample1 <- (log2sample1-meanlog2sample1)/StdDevlog2sample1; head(Normlog2sample1)## [1] 0.3788472 0.7337468 1.3437920 0.6916001 0.1403251 -0.2030074tst<- Normlog2sample1
hist(tst, breaks = nbreaks, col= 1:5,
main = 'Normalized Log2sample1',
xlab='pEhEx1',
ylab= 'Frequency pEhEx')
fw1<-fitdist(tst, "norm")
plotdist(tst, histo = TRUE, demp = TRUE)
nnorm.f <- fitdist(tst,"norm")
summary(nnorm.f)## Fitting of the distribution ' norm ' by maximum likelihood
## Parameters :
## estimate Std. Error
## mean -5.346355e-17 0.01451411
## sd 9.998946e-01 0.01026298
## Loglikelihood: -6733.782 AIC: 13471.56 BIC: 13484.49
## Correlation matrix:
## mean sd
## mean 1.000000e+00 -3.386913e-11
## sd -3.386913e-11 1.000000e+00par(mfrow=c(2,2))
denscomp(nnorm.f,legendtext = 'Dist Normal')
qqcomp(nnorm.f,legendtext = 'Dist Normal')
cdfcomp(nnorm.f,legendtext = 'Dist Normal')
ppcomp(nnorm.f,legendtext = 'Dist Normal')
probs <- c();
probs[8] = 0.175; probs[9] = 0.825;
probs[7] = 0.15; probs[10] = 0.85;
probs[6] = 0.125; probs[11] = 0.875;
probs[5] = 0.1; probs[12] = 0.9;
probs[4] = 0.075; probs[13] = 0.925;
probs[3] = 0.05; probs[14] = 0.95;
probs[2] = 0.025; probs[15] = 0.975;
probs[1] = 0.005; probs[16] = 0.995;
CuantilesData <- quantile(tst,prob = probs)
CuantilesModel <- qnorm(probs, mean=0, sd=1)
Cuantilillos <- t(CuantilesModel)
colnames(Cuantilillos) <- c('0.5%','2.5%','5%','7.5%',
'10%','12.5%','15%','17.5%',
'82.5%','85%','87.5%','90%',
'92.5%','95%','97.5%','99.5%')
Cuantilillos <- t(Cuantilillos)
colnames(Cuantilillos) <- c('Cuantiles Ajuste')
print(Cuantilillos)## Cuantiles Ajuste
## 0.5% -2.5758293
## 2.5% -1.9599640
## 5% -1.6448536
## 7.5% -1.4395315
## 10% -1.2815516
## 12.5% -1.1503494
## 15% -1.0364334
## 17.5% -0.9345893
## 82.5% 0.9345893
## 85% 1.0364334
## 87.5% 1.1503494
## 90% 1.2815516
## 92.5% 1.4395315
## 95% 1.6448536
## 97.5% 1.9599640
## 99.5% 2.5758293Cálculo de cuantiles
CuantilesA <- matrix(0,8,2)
CuantilesD <- matrix(0,8,2)
colnames(CuantilesA) <- c('LimInf','LimSup')
colnames(CuantilesD) <- c('LimInf','LimSup')
rownames(CuantilesA) <- c('65','70','75','80','85','90','95','99')
rownames(CuantilesD) <- c('65','70','75','80','85','90','95','99')
CuantilesA[1,1] <-CuantilesData[8]; CuantilesA[1,2] <-CuantilesData[9]
CuantilesA[2,1] <-CuantilesData[7]; CuantilesA[2,2] <-CuantilesData[10]
CuantilesA[3,1] <-CuantilesData[6]; CuantilesA[3,2] <-CuantilesData[11]
CuantilesA[4,1] <-CuantilesData[5]; CuantilesA[4,2] <-CuantilesData[12]
CuantilesA[5,1] <-CuantilesData[4]; CuantilesA[5,2] <-CuantilesData[13]
CuantilesA[6,1] <-CuantilesData[3]; CuantilesA[6,2] <-CuantilesData[14]
CuantilesA[7,1] <-CuantilesData[2]; CuantilesA[7,2] <-CuantilesData[15]
CuantilesA[8,1] <-CuantilesData[1]; CuantilesA[8,2] <-CuantilesData[16]
CuantilesD[1,1] <-Cuantilillos[8]; CuantilesD[1,2] <-Cuantilillos[9]
CuantilesD[2,1] <-Cuantilillos[7]; CuantilesD[2,2] <-Cuantilillos[10]
CuantilesD[3,1] <-Cuantilillos[6]; CuantilesD[3,2] <-Cuantilillos[11]
CuantilesD[4,1] <-Cuantilillos[5]; CuantilesD[4,2] <-Cuantilillos[12]
CuantilesD[5,1] <-Cuantilillos[4]; CuantilesD[5,2] <-Cuantilillos[13]
CuantilesD[6,1] <-Cuantilillos[3]; CuantilesD[6,2] <-Cuantilillos[14]
CuantilesD[7,1] <-Cuantilillos[2]; CuantilesD[7,2] <-Cuantilillos[15]
CuantilesD[8,1] <-Cuantilillos[1]; CuantilesD[8,2] <-Cuantilillos[16]print(CuantilesD)## LimInf LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293print(CuantilesA)## LimInf LimSup
## 65 -0.8625714 0.8124762
## 70 -0.9422460 0.9640896
## 75 -0.9718431 1.1342860
## 80 -1.0367684 1.3790909
## 85 -1.0726303 1.7033941
## 90 -1.1529698 2.1934547
## 95 -1.3038487 2.6233257
## 99 -1.7398510 3.1888833col_sequence <- rainbow(n = 7, alpha = 0.35, start = 0, end = 1)
par(mfrow=c(2,1))
hist(tst, breaks = nbreaks, col = col_sequence,
main = 'Normalized Log2 pEhEx - DATA', lty = 9)
abline(v=CuantilesA[1,1], lty=2, col="darkgoldenrod4"); # 65% INFERIOR
abline(v=CuantilesA[1,2], lty=2, col="darkgoldenrod4"); # 65% SUPERIOR
abline(v=CuantilesA[2,1], lty=2, col="darkblue"); # 70% INFERIOR
abline(v=CuantilesA[2,2], lty=2, col="darkblue") # 70% SUPERIOR
abline(v=CuantilesA[3,1], lty=2, col="aquamarine4"); # 75% INFERIOR
abline(v=CuantilesA[3,2], lty=2, col="aquamarine4"); # 75% SUPERIOR
abline(v=CuantilesA[4,1], lty=2, col="green"); # 80% INFERIOR
abline(v=CuantilesA[4,2], lty=2, col="green"); # 80% SUPERIOR
abline(v=CuantilesA[5,1], lty=2, col="brown"); # 85% INFERIOR
abline(v=CuantilesA[5,2], lty=2, col="brown"); # 85% SUPERIOR
abline(v=CuantilesA[6,1], lty=2, col="red"); # 90% INFERIOR
abline(v=CuantilesA[6,2], lty=2, col="red"); # 90% SUPERIOR
abline(v=CuantilesA[7,1], lty=2, col="blue"); # 95% INFERIOR
abline(v=CuantilesA[7,2], lty=2, col="blue"); # 95% SUPERIOR
abline(v=CuantilesA[8,1], lty=2, col="orange"); # 99% INFERIOR
abline(v=CuantilesA[8,2], lty=2, col="orange"); # 99% SUPERIOR
legend("topright",
legend=c("65%","70%","75%","80%","85%","90%","95%","99%"),
pch=c(1,2,3,4,5,6,7,8),
col=c("darkgoldenrod4","darkblue","aquamarine4",
"green", "brown","red","blue","orange"))
hist(tst, breaks = nbreaks, col = col_sequence,
main = 'Normalized Log2 pEhEx - ADJUSTED', lty=9)
abline(v=CuantilesD[1,1], lty=2, col="darkgoldenrod4"); # 65% INFERIOR
abline(v=CuantilesD[1,2], lty=2, col="darkgoldenrod4"); # 65% SUPERIOR
abline(v=CuantilesD[2,1], lty=2, col="darkblue"); # 70% INFERIOR
abline(v=CuantilesD[2,2], lty=2, col="darkblue"); # 70% SUPERIOR
abline(v=CuantilesD[3,1], lty=2, col="aquamarine4"); # 75% INFERIOR
abline(v=CuantilesD[3,2], lty=2, col="aquamarine4"); # 75% SUPERIOR
abline(v=CuantilesD[4,1], lty=2, col="green"); # 80% INFERIOR
abline(v=CuantilesD[4,2], lty=2, col="green"); # 80% SUPERIOR
abline(v=CuantilesD[5,1], lty=2, col="brown"); # 85% INFERIOR
abline(v=CuantilesD[5,2], lty=2, col="brown"); # 85% SUPERIOR
abline(v=CuantilesD[6,1], lty=2, col="red"); # 90% INFERIOR
abline(v=CuantilesD[6,2], lty=2, col="red"); # 90% SUPERIOR
abline(v=CuantilesD[7,1], lty=2, col="blue"); # 95% INFERIOR
abline(v=CuantilesD[7,2], lty=2, col="blue"); # 95% SUPERIOR
abline(v=CuantilesD[8,1], lty=2, col="orange"); # 99% INFERIOR
abline(v=CuantilesD[8,2], lty=2, col="orange"); # 99% SUPERIOR
legend("topright",
legend=c("65%","70%","75%","80%","85%","90%","95%","99%"),
pch=c(1,2,3,4,5,6,7,8),
col=c("darkgoldenrod4","darkblue","aquamarine4",
"green", "brown","red","blue","orange"))
Grafica Cuantiles del \(65\%\) y \(80\%\)
par(mfrow=c(2,1))
hist(tst, breaks = nbreaks, col = col_sequence,
main = 'Normalized Log2 pEhEx - DATA', lty=9)
abline(v=CuantilesA[1,1], lty=2, col="darkgoldenrod4"); # 65% INFERIOR
abline(v=CuantilesA[1,2], lty=2, col="darkgoldenrod4"); # 65% SUPERIOR
abline(v=CuantilesA[4,1], lty=2, col="green"); # 80% INFERIOR
abline(v=CuantilesA[4,2], lty=2, col="green"); # 80% SUPERIOR
legend("topright",legend=c("65%","80%"),
pch=c(1,2),col=c("darkgoldenrod4","green"))
hist(tst, breaks = nbreaks, col = col_sequence,
main = 'Normalized Log2 BaseMean - ADJUSTED', lty=9)
abline(v=CuantilesD[1,1], lty=2, col="darkgoldenrod4"); # 65% INFERIOR
abline(v=CuantilesD[1,2], lty=2, col="darkgoldenrod4"); # 65% SUPERIOR
abline(v=CuantilesD[4,1], lty=2, col="green"); # 80% INFERIOR
abline(v=CuantilesD[4,2], lty=2, col="green"); # 80% SUPERIOR
legend("topright",legend=c("65%","80%"),
pch=c(1,2),col=c("darkgoldenrod4","green"))
Grafica Cuantiles del \(70\%\) y \(85\%\)
par(mfrow=c(2,1))
hist(tst, breaks = nbreaks, col = col_sequence,
main = 'Normalized Log2 pEhEx - DATA', lty=9)
abline(v=CuantilesA[2,1], lty=2, col="darkblue");
abline(v=CuantilesA[2,2], lty=2, col="darkblue")
abline(v=CuantilesA[5,1], lty=2, col="brown");
abline(v=CuantilesA[5,2], lty=2, col="brown")
legend("topright",legend=c("70%","85%"),
pch=c(1,2),#3,4,5,6,7,8),
col=c("brown"))
hist(tst, breaks = nbreaks, col = col_sequence,
main = 'Normalized Log2 pEhEx - ADJUSTED', lty=9)
abline(v=CuantilesD[2,1], lty=2, col="darkblue");
abline(v=CuantilesD[2,2], lty=2, col="darkblue")
abline(v=CuantilesD[5,1], lty=2, col="brown");
abline(v=CuantilesD[5,2], lty=2, col="brown")
legend("topright",legend=c("70%","85%"),
pch=c(1,2),#3,4,5,6,7,8),
col=c("brown"))
Muestra 2
log2sample2 <- data4$log2sample2; head(mean(log2sample2)); head(sd(log2sample2))## [1] 6.018835## [1] 3.085311head(log2sample2,5)## [1] 8.442638 8.100100 10.598849 8.039359 5.774415ndata4 <- length(log2sample2)
hist(log2sample2, breaks = nbreaks, col= rainbow(25,0.3),
main = 'Log2 sample2')
Log-normalizacion
meanlog2sample2 <- mean(log2sample2); head(meanlog2sample2)## [1] 6.018835StdDevlog2sample2 <- sd(log2sample2); head(StdDevlog2sample2)## [1] 3.085311Normlog2sample2 <- (log2sample2-meanlog2sample2)/StdDevlog2sample2; head(Normlog2sample2)## [1] 0.78559421 0.67457202 1.48445747 0.65488484 -0.07922075 0.02179028tst<- Normlog2sample2hist(tst, breaks = nbreaks, col= 1:5,
main = 'Normalized Log2sample1',
xlab='pEhEx1',
ylab= 'Frequency pEhEx')
Ajuste de modelo
fw1<-fitdist(tst, "norm")
plotdist(tst, histo = TRUE, demp = TRUE)
nnorm.f <- fitdist(tst,"norm")
summary(nnorm.f)## Fitting of the distribution ' norm ' by maximum likelihood
## Parameters :
## estimate Std. Error
## mean 4.519200e-17 0.01451411
## sd 9.998946e-01 0.01026298
## Loglikelihood: -6733.782 AIC: 13471.56 BIC: 13484.49
## Correlation matrix:
## mean sd
## mean 1 0
## sd 0 1par(mfrow=c(2,2))
denscomp(nnorm.f,legendtext = 'Dist Normal')
qqcomp(nnorm.f,legendtext = 'Dist Normal')
cdfcomp(nnorm.f,legendtext = 'Dist Normal')
ppcomp(nnorm.f,legendtext = 'Dist Normal')
probs <- c();
probs[8] = 0.175; probs[9] = 0.825;
probs[7] = 0.15; probs[10] = 0.85;
probs[6] = 0.125; probs[11] = 0.875;
probs[5] = 0.1; probs[12] = 0.9;
probs[4] = 0.075; probs[13] = 0.925;
probs[3] = 0.05; probs[14] = 0.95;
probs[2] = 0.025; probs[15] = 0.975;
probs[1] = 0.005; probs[16] = 0.995; CuantilesData <- quantile(tst,prob = probs)
CuantilesModel <- qnorm(probs, mean=0, sd=1)
Cuantilillos <- t(CuantilesModel)
colnames(Cuantilillos) <- c('0.5%','2.5%','5%','7.5%',
'10%','12.5%','15%','17.5%',
'82.5%','85%','87.5%','90%',
'92.5%','95%','97.5%','99.5%')
Cuantilillos <- t(Cuantilillos)
colnames(Cuantilillos) <- c('Cuantiles Ajuste')
print(Cuantilillos)## Cuantiles Ajuste
## 0.5% -2.5758293
## 2.5% -1.9599640
## 5% -1.6448536
## 7.5% -1.4395315
## 10% -1.2815516
## 12.5% -1.1503494
## 15% -1.0364334
## 17.5% -0.9345893
## 82.5% 0.9345893
## 85% 1.0364334
## 87.5% 1.1503494
## 90% 1.2815516
## 92.5% 1.4395315
## 95% 1.6448536
## 97.5% 1.9599640
## 99.5% 2.5758293CuantilesA <- matrix(0,8,2)
CuantilesD <- matrix(0,8,2)
colnames(CuantilesA) <- c('LimInf','LimSup')
colnames(CuantilesD) <- c('LimInf','LimSup')
rownames(CuantilesA) <- c('65','70','75','80','85','90','95','99')
rownames(CuantilesD) <- c('65','70','75','80','85','90','95','99')
CuantilesA[1,1] <-CuantilesData[8]; CuantilesA[1,2] <-CuantilesData[9]
CuantilesA[2,1] <-CuantilesData[7]; CuantilesA[2,2] <-CuantilesData[10]
CuantilesA[3,1] <-CuantilesData[6]; CuantilesA[3,2] <-CuantilesData[11]
CuantilesA[4,1] <-CuantilesData[5]; CuantilesA[4,2] <-CuantilesData[12]
CuantilesA[5,1] <-CuantilesData[4]; CuantilesA[5,2] <-CuantilesData[13]
CuantilesA[6,1] <-CuantilesData[3]; CuantilesA[6,2] <-CuantilesData[14]
CuantilesA[7,1] <-CuantilesData[2]; CuantilesA[7,2] <-CuantilesData[15]
CuantilesA[8,1] <-CuantilesData[1]; CuantilesA[8,2] <-CuantilesData[16]
CuantilesD[1,1] <-Cuantilillos[8]; CuantilesD[1,2] <-Cuantilillos[9]
CuantilesD[2,1] <-Cuantilillos[7]; CuantilesD[2,2] <-Cuantilillos[10]
CuantilesD[3,1] <-Cuantilillos[6]; CuantilesD[3,2] <-Cuantilillos[11]
CuantilesD[4,1] <-Cuantilillos[5]; CuantilesD[4,2] <-Cuantilillos[12]
CuantilesD[5,1] <-Cuantilillos[4]; CuantilesD[5,2] <-Cuantilillos[13]
CuantilesD[6,1] <-Cuantilillos[3]; CuantilesD[6,2] <-Cuantilillos[14]
CuantilesD[7,1] <-Cuantilillos[2]; CuantilesD[7,2] <-Cuantilillos[15]
CuantilesD[8,1] <-Cuantilillos[1]; CuantilesD[8,2] <-Cuantilillos[16]
print(CuantilesD)## LimInf LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293print(CuantilesA)## LimInf LimSup
## 65 -0.8988548 0.8463966
## 70 -0.9478079 0.9891035
## 75 -1.0024917 1.1467315
## 80 -1.0644280 1.3749391
## 85 -1.1358390 1.6937524
## 90 -1.2201564 2.1241067
## 95 -1.4552922 2.5236980
## 99 -1.9508033 3.0984441col_sequence <- rainbow(n = 7, alpha = 0.35, start = 0, end = 1)
par(mfrow=c(2,1))
hist(tst, breaks = nbreaks, col = col_sequence,
main = 'Normalized Log2 pEhEx - DATA (sample 2)', lty = 9)
abline(v=CuantilesA[1,1], lty=2, col="darkgoldenrod4"); # 65% INFERIOR
abline(v=CuantilesA[1,2], lty=2, col="darkgoldenrod4"); # 65% SUPERIOR
abline(v=CuantilesA[2,1], lty=2, col="darkblue"); # 70% INFERIOR
abline(v=CuantilesA[2,2], lty=2, col="darkblue") # 70% SUPERIOR
abline(v=CuantilesA[3,1], lty=2, col="aquamarine4"); # 75% INFERIOR
abline(v=CuantilesA[3,2], lty=2, col="aquamarine4"); # 75% SUPERIOR
abline(v=CuantilesA[4,1], lty=2, col="green"); # 80% INFERIOR
abline(v=CuantilesA[4,2], lty=2, col="green"); # 80% SUPERIOR
abline(v=CuantilesA[5,1], lty=2, col="brown"); # 85% INFERIOR
abline(v=CuantilesA[5,2], lty=2, col="brown"); # 85% SUPERIOR
abline(v=CuantilesA[6,1], lty=2, col="red"); # 90% INFERIOR
abline(v=CuantilesA[6,2], lty=2, col="red"); # 90% SUPERIOR
abline(v=CuantilesA[7,1], lty=2, col="blue"); # 95% INFERIOR
abline(v=CuantilesA[7,2], lty=2, col="blue"); # 95% SUPERIOR
abline(v=CuantilesA[8,1], lty=2, col="orange"); # 99% INFERIOR
abline(v=CuantilesA[8,2], lty=2, col="orange"); # 99% SUPERIOR
legend("topright",
legend=c("65%","70%","75%","80%","85%","90%","95%","99%"),
pch=c(1,2,3,4,5,6,7,8),
col=c("darkgoldenrod4","darkblue","aquamarine4",
"green", "brown","red","blue","orange"))
hist(tst, breaks = nbreaks, col = col_sequence,
main = 'Normalized Log2 pEhEx - ADJUSTED (sample 2)', lty=9)
abline(v=CuantilesD[1,1], lty=2, col="darkgoldenrod4"); # 65% INFERIOR
abline(v=CuantilesD[1,2], lty=2, col="darkgoldenrod4"); # 65% SUPERIOR
abline(v=CuantilesD[2,1], lty=2, col="darkblue"); # 70% INFERIOR
abline(v=CuantilesD[2,2], lty=2, col="darkblue"); # 70% SUPERIOR
abline(v=CuantilesD[3,1], lty=2, col="aquamarine4"); # 75% INFERIOR
abline(v=CuantilesD[3,2], lty=2, col="aquamarine4"); # 75% SUPERIOR
abline(v=CuantilesD[4,1], lty=2, col="green"); # 80% INFERIOR
abline(v=CuantilesD[4,2], lty=2, col="green"); # 80% SUPERIOR
abline(v=CuantilesD[5,1], lty=2, col="brown"); # 85% INFERIOR
abline(v=CuantilesD[5,2], lty=2, col="brown"); # 85% SUPERIOR
abline(v=CuantilesD[6,1], lty=2, col="red"); # 90% INFERIOR
abline(v=CuantilesD[6,2], lty=2, col="red"); # 90% SUPERIOR
abline(v=CuantilesD[7,1], lty=2, col="blue"); # 95% INFERIOR
abline(v=CuantilesD[7,2], lty=2, col="blue"); # 95% SUPERIOR
abline(v=CuantilesD[8,1], lty=2, col="orange"); # 99% INFERIOR
abline(v=CuantilesD[8,2], lty=2, col="orange"); # 99% SUPERIOR
legend("topright",
legend=c("65%","70%","75%","80%","85%","90%","95%","99%"),
pch=c(1,2,3,4,5,6,7,8),
col=c("darkgoldenrod4","darkblue","aquamarine4",
"green", "brown","red","blue","orange"))
Grafica Cuantiles del \(65\%\) y \(80\%\)
par(mfrow=c(2,1))
hist(tst, breaks = nbreaks, col = col_sequence,
main = 'Normalized Log2 pEhEx - DATA (sample 2)', lty=9)
abline(v=CuantilesA[1,1], lty=2, col="darkgoldenrod4"); # 65% INFERIOR
abline(v=CuantilesA[1,2], lty=2, col="darkgoldenrod4"); # 65% SUPERIOR
abline(v=CuantilesA[4,1], lty=2, col="green"); # 80% INFERIOR
abline(v=CuantilesA[4,2], lty=2, col="green"); # 80% SUPERIOR
legend("topright",legend=c("65%","80%"),
pch=c(1,2),col=c("darkgoldenrod4","green"))
hist(tst, breaks = nbreaks, col = col_sequence,
main = 'Normalized Log2 BaseMean - ADJUSTED (sample 2)', lty=9)
abline(v=CuantilesD[1,1], lty=2, col="darkgoldenrod4"); # 65% INFERIOR
abline(v=CuantilesD[1,2], lty=2, col="darkgoldenrod4"); # 65% SUPERIOR
abline(v=CuantilesD[4,1], lty=2, col="green"); # 80% INFERIOR
abline(v=CuantilesD[4,2], lty=2, col="green"); # 80% SUPERIOR
legend("topright",legend=c("65%","80%"),
pch=c(1,2),col=c("darkgoldenrod4","green"))
Grafica Cuantiles del \(70\%\) y \(85\%\)
par(mfrow=c(2,1))
hist(tst, breaks = nbreaks, col = col_sequence,
main = 'Normalized Log2 pEhEx - DATA (sample 2)', lty=9)
abline(v=CuantilesA[2,1], lty=2, col="darkblue");
abline(v=CuantilesA[2,2], lty=2, col="darkblue")
abline(v=CuantilesA[5,1], lty=2, col="brown");
abline(v=CuantilesA[5,2], lty=2, col="brown")
legend("topright",legend=c("70%","85%"),
pch=c(1,2),#3,4,5,6,7,8),
col=c("brown"))
hist(tst, breaks = nbreaks, col = col_sequence,
main = 'Normalized Log2 pEhEx - ADJUSTED (sample 2)', lty=9)
abline(v=CuantilesD[2,1], lty=2, col="darkblue");
abline(v=CuantilesD[2,2], lty=2, col="darkblue")
abline(v=CuantilesD[5,1], lty=2, col="brown");
abline(v=CuantilesD[5,2], lty=2, col="brown")
legend("topright",legend=c("70%","85%"),
pch=c(1,2),#3,4,5,6,7,8),
col=c("brown"))
Muestra 3
log2sample3 <- data4$log2sample3; head(mean(log2sample3)); head(sd(log2sample3))## [1] 5.938851## [1] 3.137257head(log2sample3,5)## [1] 8.580256 4.997864 10.726898 9.040624 5.361902ndata4 <- length(log2sample3)
hist(log2sample3, breaks = nbreaks, col= rainbow(25,0.3),
main = 'Log2 sample2')
meanlog2sample3 <- mean(log2sample3); head(meanlog2sample3)## [1] 5.938851StdDevlog2sample3 <- sd(log2sample3); head(StdDevlog2sample3)## [1] 3.137257Normlog2sample3 <- (log2sample3-meanlog2sample3)/StdDevlog2sample3; head(Normlog2sample3)## [1] 0.84194733 -0.29993956 1.52618884 0.98868937 -0.18390246 -0.09132474tst<- Normlog2sample3hist(tst, breaks = nbreaks, col= 1:5,
main = 'Normalized Log2sample1',
xlab='pEhEx1',
ylab= 'Frequency pEhEx')
Ajustando Modelos
fw1<-fitdist(tst, "norm")
plotdist(tst, histo = TRUE, demp = TRUE)
nnorm.f <- fitdist(tst,"norm")
summary(nnorm.f)## Fitting of the distribution ' norm ' by maximum likelihood
## Parameters :
## estimate Std. Error
## mean 1.897633e-17 0.01451411
## sd 9.998946e-01 0.01026298
## Loglikelihood: -6733.782 AIC: 13471.56 BIC: 13484.49
## Correlation matrix:
## mean sd
## mean 1 0
## sd 0 1par(mfrow=c(2,2))
denscomp(nnorm.f,legendtext = 'Dist Normal')
qqcomp(nnorm.f,legendtext = 'Dist Normal')
cdfcomp(nnorm.f,legendtext = 'Dist Normal')
ppcomp(nnorm.f,legendtext = 'Dist Normal')
probs <- c();
probs[8] = 0.175; probs[9] = 0.825;
probs[7] = 0.15; probs[10] = 0.85;
probs[6] = 0.125; probs[11] = 0.875;
probs[5] = 0.1; probs[12] = 0.9;
probs[4] = 0.075; probs[13] = 0.925;
probs[3] = 0.05; probs[14] = 0.95;
probs[2] = 0.025; probs[15] = 0.975;
probs[1] = 0.005; probs[16] = 0.995;
CuantilesData <- quantile(tst,prob = probs)
CuantilesModel <- qnorm(probs, mean=0, sd=1)
Cuantilillos <- t(CuantilesModel)
colnames(Cuantilillos) <- c('0.5%','2.5%','5%','7.5%',
'10%','12.5%','15%','17.5%',
'82.5%','85%','87.5%','90%',
'92.5%','95%','97.5%','99.5%')
Cuantilillos <- t(Cuantilillos)
colnames(Cuantilillos) <- c('Cuantiles Ajuste')
print(Cuantilillos)## Cuantiles Ajuste
## 0.5% -2.5758293
## 2.5% -1.9599640
## 5% -1.6448536
## 7.5% -1.4395315
## 10% -1.2815516
## 12.5% -1.1503494
## 15% -1.0364334
## 17.5% -0.9345893
## 82.5% 0.9345893
## 85% 1.0364334
## 87.5% 1.1503494
## 90% 1.2815516
## 92.5% 1.4395315
## 95% 1.6448536
## 97.5% 1.9599640
## 99.5% 2.5758293CuantilesA <- matrix(0,8,2)
CuantilesD <- matrix(0,8,2)
colnames(CuantilesA) <- c('LimInf','LimSup')
colnames(CuantilesD) <- c('LimInf','LimSup')
rownames(CuantilesA) <- c('65','70','75','80','85','90','95','99')
rownames(CuantilesD) <- c('65','70','75','80','85','90','95','99')
CuantilesA[1,1] <-CuantilesData[8]; CuantilesA[1,2] <-CuantilesData[9]
CuantilesA[2,1] <-CuantilesData[7]; CuantilesA[2,2] <-CuantilesData[10]
CuantilesA[3,1] <-CuantilesData[6]; CuantilesA[3,2] <-CuantilesData[11]
CuantilesA[4,1] <-CuantilesData[5]; CuantilesA[4,2] <-CuantilesData[12]
CuantilesA[5,1] <-CuantilesData[4]; CuantilesA[5,2] <-CuantilesData[13]
CuantilesA[6,1] <-CuantilesData[3]; CuantilesA[6,2] <-CuantilesData[14]
CuantilesA[7,1] <-CuantilesData[2]; CuantilesA[7,2] <-CuantilesData[15]
CuantilesA[8,1] <-CuantilesData[1]; CuantilesA[8,2] <-CuantilesData[16]
CuantilesD[1,1] <-Cuantilillos[8]; CuantilesD[1,2] <-Cuantilillos[9]
CuantilesD[2,1] <-Cuantilillos[7]; CuantilesD[2,2] <-Cuantilillos[10]
CuantilesD[3,1] <-Cuantilillos[6]; CuantilesD[3,2] <-Cuantilillos[11]
CuantilesD[4,1] <-Cuantilillos[5]; CuantilesD[4,2] <-Cuantilillos[12]
CuantilesD[5,1] <-Cuantilillos[4]; CuantilesD[5,2] <-Cuantilillos[13]
CuantilesD[6,1] <-Cuantilillos[3]; CuantilesD[6,2] <-Cuantilillos[14]
CuantilesD[7,1] <-Cuantilillos[2]; CuantilesD[7,2] <-Cuantilillos[15]
CuantilesD[8,1] <-Cuantilillos[1]; CuantilesD[8,2] <-Cuantilillos[16]
print(CuantilesD)## LimInf LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293print(CuantilesA)## LimInf LimSup
## 65 -0.8813312 0.8377086
## 70 -0.9785407 0.9709584
## 75 -1.0570406 1.1167404
## 80 -1.1517563 1.3292508
## 85 -1.2711789 1.6268787
## 90 -1.4329274 2.0299674
## 95 -1.5417724 2.4960043
## 99 -1.8930077 3.1029256Creación de histogramas
col_sequence <- rainbow(n = 7, alpha = 0.35, start = 0, end = 1)
par(mfrow=c(2,1))
hist(tst, breaks = nbreaks, col = col_sequence,
main = 'Normalized Log2 pEhEx - DATA (sample 3)', lty = 9)
abline(v=CuantilesA[1,1], lty=2, col="darkgoldenrod4"); # 65% INFERIOR
abline(v=CuantilesA[1,2], lty=2, col="darkgoldenrod4"); # 65% SUPERIOR
abline(v=CuantilesA[2,1], lty=2, col="darkblue"); # 70% INFERIOR
abline(v=CuantilesA[2,2], lty=2, col="darkblue") # 70% SUPERIOR
abline(v=CuantilesA[3,1], lty=2, col="aquamarine4"); # 75% INFERIOR
abline(v=CuantilesA[3,2], lty=2, col="aquamarine4"); # 75% SUPERIOR
abline(v=CuantilesA[4,1], lty=2, col="green"); # 80% INFERIOR
abline(v=CuantilesA[4,2], lty=2, col="green"); # 80% SUPERIOR
abline(v=CuantilesA[5,1], lty=2, col="brown"); # 85% INFERIOR
abline(v=CuantilesA[5,2], lty=2, col="brown"); # 85% SUPERIOR
abline(v=CuantilesA[6,1], lty=2, col="red"); # 90% INFERIOR
abline(v=CuantilesA[6,2], lty=2, col="red"); # 90% SUPERIOR
abline(v=CuantilesA[7,1], lty=2, col="blue"); # 95% INFERIOR
abline(v=CuantilesA[7,2], lty=2, col="blue"); # 95% SUPERIOR
abline(v=CuantilesA[8,1], lty=2, col="orange"); # 99% INFERIOR
abline(v=CuantilesA[8,2], lty=2, col="orange"); # 99% SUPERIOR
legend("topright",
legend=c("65%","70%","75%","80%","85%","90%","95%","99%"),
pch=c(1,2,3,4,5,6,7,8),
col=c("darkgoldenrod4","darkblue","aquamarine4",
"green", "brown","red","blue","orange"))
hist(tst, breaks = nbreaks, col = col_sequence,
main = 'Normalized Log2 pEhEx - ADJUSTED (sample 3)', lty=9)
abline(v=CuantilesD[1,1], lty=2, col="darkgoldenrod4"); # 65% INFERIOR
abline(v=CuantilesD[1,2], lty=2, col="darkgoldenrod4"); # 65% SUPERIOR
abline(v=CuantilesD[2,1], lty=2, col="darkblue"); # 70% INFERIOR
abline(v=CuantilesD[2,2], lty=2, col="darkblue"); # 70% SUPERIOR
abline(v=CuantilesD[3,1], lty=2, col="aquamarine4"); # 75% INFERIOR
abline(v=CuantilesD[3,2], lty=2, col="aquamarine4"); # 75% SUPERIOR
abline(v=CuantilesD[4,1], lty=2, col="green"); # 80% INFERIOR
abline(v=CuantilesD[4,2], lty=2, col="green"); # 80% SUPERIOR
abline(v=CuantilesD[5,1], lty=2, col="brown"); # 85% INFERIOR
abline(v=CuantilesD[5,2], lty=2, col="brown"); # 85% SUPERIOR
abline(v=CuantilesD[6,1], lty=2, col="red"); # 90% INFERIOR
abline(v=CuantilesD[6,2], lty=2, col="red"); # 90% SUPERIOR
abline(v=CuantilesD[7,1], lty=2, col="blue"); # 95% INFERIOR
abline(v=CuantilesD[7,2], lty=2, col="blue"); # 95% SUPERIOR
abline(v=CuantilesD[8,1], lty=2, col="orange"); # 99% INFERIOR
abline(v=CuantilesD[8,2], lty=2, col="orange"); # 99% SUPERIOR
legend("topright",
legend=c("65%","70%","75%","80%","85%","90%","95%","99%"),
pch=c(1,2,3,4,5,6,7,8),
col=c("darkgoldenrod4","darkblue","aquamarine4",
"green", "brown","red","blue","orange"))
Grafica Cuantiles del \(65\%\) y \(80\%\)
par(mfrow=c(2,1))
hist(tst, breaks = nbreaks, col = col_sequence,
main = 'Normalized Log2 pEhEx - DATA (sample 2)', lty=9)
abline(v=CuantilesA[1,1], lty=2, col="darkgoldenrod4"); # 65% INFERIOR
abline(v=CuantilesA[1,2], lty=2, col="darkgoldenrod4"); # 65% SUPERIOR
abline(v=CuantilesA[4,1], lty=2, col="green"); # 80% INFERIOR
abline(v=CuantilesA[4,2], lty=2, col="green"); # 80% SUPERIOR
legend("topright",legend=c("65%","80%"),
pch=c(1,2),col=c("darkgoldenrod4","green"))
hist(tst, breaks = nbreaks, col = col_sequence,
main = 'Normalized Log2 BaseMean - ADJUSTED (sample 3)', lty=9)
abline(v=CuantilesD[1,1], lty=2, col="darkgoldenrod4"); # 65% INFERIOR
abline(v=CuantilesD[1,2], lty=2, col="darkgoldenrod4"); # 65% SUPERIOR
abline(v=CuantilesD[4,1], lty=2, col="green"); # 80% INFERIOR
abline(v=CuantilesD[4,2], lty=2, col="green"); # 80% SUPERIOR
legend("topright",legend=c("65%","80%"),
pch=c(1,2),col=c("darkgoldenrod4","green"))
Grafica Cuantiles del \(70\%\) y \(85\%\)
par(mfrow=c(2,1))
hist(tst, breaks = nbreaks, col = col_sequence,
main = 'Normalized Log2 pEhEx - DATA (sample 3)', lty=9)
abline(v=CuantilesA[2,1], lty=2, col="darkblue");
abline(v=CuantilesA[2,2], lty=2, col="darkblue")
abline(v=CuantilesA[5,1], lty=2, col="brown");
abline(v=CuantilesA[5,2], lty=2, col="brown")
legend("topright",legend=c("70%","85%"),
pch=c(1,2),#3,4,5,6,7,8),
col=c("brown"))
hist(tst, breaks = nbreaks, col = col_sequence,
main = 'Normalized Log2 pEhEx - ADJUSTED (sample 3)', lty=9)
abline(v=CuantilesD[2,1], lty=2, col="darkblue");
abline(v=CuantilesD[2,2], lty=2, col="darkblue")
abline(v=CuantilesD[5,1], lty=2, col="brown");
abline(v=CuantilesD[5,2], lty=2, col="brown")
legend("topright",legend=c("70%","85%"),
pch=c(1,2),#3,4,5,6,7,8),
col=c("brown"))
Muestra pEhExvsCDC51
log2vsCDC51 <- data4$log2samplevsCDC51; head(mean(log2vsCDC51)); head(sd(log2vsCDC51))## [1] 6.073889## [1] 2.856279head(log2vsCDC51,5)## [1] 5.921721 6.897596 9.430800 8.739559 6.715376ndata4 <- length(log2vsCDC51)
hist(log2vsCDC51, breaks = nbreaks, col= rainbow(25,0.3),
main = 'Log2vsCDC51')
meanlog2vsCDC51 <- mean(log2vsCDC51); head(meanlog2vsCDC51)## [1] 6.073889StdDevlog2vsCDC51 <- sd(log2vsCDC51); head(StdDevlog2vsCDC51)## [1] 2.856279Normlog2vsCDC51 <- (log2vsCDC51-meanlog2vsCDC51)/StdDevlog2vsCDC51; head(Normlog2vsCDC51)## [1] -0.05327506 0.28838461 1.17527408 0.93326643 0.22458816 -0.24564228tst<- Normlog2vsCDC51Primer histograma
hist(tst, breaks = nbreaks, col= 1:5,
main = 'Normalized Log2vsCDC51',
xlab='pEhEx1',
ylab= 'Frequency pEhEx')
Ajustando modelo
fw1<-fitdist(tst, "norm")
plotdist(tst, histo = TRUE, demp = TRUE)
nnorm.f <- fitdist(tst,"norm")
summary(nnorm.f)## Fitting of the distribution ' norm ' by maximum likelihood
## Parameters :
## estimate Std. Error
## mean -1.363897e-18 0.01451411
## sd 9.998946e-01 0.01026298
## Loglikelihood: -6733.782 AIC: 13471.56 BIC: 13484.49
## Correlation matrix:
## mean sd
## mean 1 0
## sd 0 1par(mfrow=c(2,2))
denscomp(nnorm.f,legendtext = 'Dist Normal')
qqcomp(nnorm.f,legendtext = 'Dist Normal')
cdfcomp(nnorm.f,legendtext = 'Dist Normal')
ppcomp(nnorm.f,legendtext = 'Dist Normal')
probs <- c();
probs[8] = 0.175; probs[9] = 0.825;
probs[7] = 0.15; probs[10] = 0.85;
probs[6] = 0.125; probs[11] = 0.875;
probs[5] = 0.1; probs[12] = 0.9;
probs[4] = 0.075; probs[13] = 0.925;
probs[3] = 0.05; probs[14] = 0.95;
probs[2] = 0.025; probs[15] = 0.975;
probs[1] = 0.005; probs[16] = 0.995;
CuantilesData <- quantile(tst,prob = probs)
CuantilesModel <- qnorm(probs, mean=0, sd=1)
Cuantilillos <- t(CuantilesModel)
colnames(Cuantilillos) <- c('0.5%','2.5%','5%','7.5%',
'10%','12.5%','15%','17.5%',
'82.5%','85%','87.5%','90%',
'92.5%','95%','97.5%','99.5%')
Cuantilillos <- t(Cuantilillos)
colnames(Cuantilillos) <- c('Cuantiles Ajuste')
print(Cuantilillos)## Cuantiles Ajuste
## 0.5% -2.5758293
## 2.5% -1.9599640
## 5% -1.6448536
## 7.5% -1.4395315
## 10% -1.2815516
## 12.5% -1.1503494
## 15% -1.0364334
## 17.5% -0.9345893
## 82.5% 0.9345893
## 85% 1.0364334
## 87.5% 1.1503494
## 90% 1.2815516
## 92.5% 1.4395315
## 95% 1.6448536
## 97.5% 1.9599640
## 99.5% 2.5758293CuantilesA <- matrix(0,8,2)
CuantilesD <- matrix(0,8,2)
colnames(CuantilesA) <- c('LimInf','LimSup')
colnames(CuantilesD) <- c('LimInf','LimSup')
rownames(CuantilesA) <- c('65','70','75','80','85','90','95','99')
rownames(CuantilesD) <- c('65','70','75','80','85','90','95','99')
CuantilesA[1,1] <-CuantilesData[8]; CuantilesA[1,2] <-CuantilesData[9]
CuantilesA[2,1] <-CuantilesData[7]; CuantilesA[2,2] <-CuantilesData[10]
CuantilesA[3,1] <-CuantilesData[6]; CuantilesA[3,2] <-CuantilesData[11]
CuantilesA[4,1] <-CuantilesData[5]; CuantilesA[4,2] <-CuantilesData[12]
CuantilesA[5,1] <-CuantilesData[4]; CuantilesA[5,2] <-CuantilesData[13]
CuantilesA[6,1] <-CuantilesData[3]; CuantilesA[6,2] <-CuantilesData[14]
CuantilesA[7,1] <-CuantilesData[2]; CuantilesA[7,2] <-CuantilesData[15]
CuantilesA[8,1] <-CuantilesData[1]; CuantilesA[8,2] <-CuantilesData[16]
CuantilesD[1,1] <-Cuantilillos[8]; CuantilesD[1,2] <-Cuantilillos[9]
CuantilesD[2,1] <-Cuantilillos[7]; CuantilesD[2,2] <-Cuantilillos[10]
CuantilesD[3,1] <-Cuantilillos[6]; CuantilesD[3,2] <-Cuantilillos[11]
CuantilesD[4,1] <-Cuantilillos[5]; CuantilesD[4,2] <-Cuantilillos[12]
CuantilesD[5,1] <-Cuantilillos[4]; CuantilesD[5,2] <-Cuantilillos[13]
CuantilesD[6,1] <-Cuantilillos[3]; CuantilesD[6,2] <-Cuantilillos[14]
CuantilesD[7,1] <-Cuantilillos[2]; CuantilesD[7,2] <-Cuantilillos[15]
CuantilesD[8,1] <-Cuantilillos[1]; CuantilesD[8,2] <-Cuantilillos[16]
print(CuantilesD)## LimInf LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293print(CuantilesA)## LimInf LimSup
## 65 -0.8665460 0.8005323
## 70 -0.9105349 0.9400738
## 75 -0.9587235 1.1113009
## 80 -1.0119991 1.3461767
## 85 -1.0715638 1.6701383
## 90 -1.0715638 2.1555034
## 95 -1.3093554 2.7085444
## 99 -2.1265041 3.2907409Histogramas
col_sequence <- rainbow(n = 7, alpha = 0.35, start = 0, end = 1)
par(mfrow=c(2,1))
hist(tst, breaks = nbreaks, col = col_sequence,
main = 'Normalized Log2vsCDC51 pEhEx - DATA', lty = 9)
abline(v=CuantilesA[1,1], lty=2, col="darkgoldenrod4"); # 65% INFERIOR
abline(v=CuantilesA[1,2], lty=2, col="darkgoldenrod4"); # 65% SUPERIOR
abline(v=CuantilesA[2,1], lty=2, col="darkblue"); # 70% INFERIOR
abline(v=CuantilesA[2,2], lty=2, col="darkblue") # 70% SUPERIOR
abline(v=CuantilesA[3,1], lty=2, col="aquamarine4"); # 75% INFERIOR
abline(v=CuantilesA[3,2], lty=2, col="aquamarine4"); # 75% SUPERIOR
abline(v=CuantilesA[4,1], lty=2, col="green"); # 80% INFERIOR
abline(v=CuantilesA[4,2], lty=2, col="green"); # 80% SUPERIOR
abline(v=CuantilesA[5,1], lty=2, col="brown"); # 85% INFERIOR
abline(v=CuantilesA[5,2], lty=2, col="brown"); # 85% SUPERIOR
abline(v=CuantilesA[6,1], lty=2, col="red"); # 90% INFERIOR
abline(v=CuantilesA[6,2], lty=2, col="red"); # 90% SUPERIOR
abline(v=CuantilesA[7,1], lty=2, col="blue"); # 95% INFERIOR
abline(v=CuantilesA[7,2], lty=2, col="blue"); # 95% SUPERIOR
abline(v=CuantilesA[8,1], lty=2, col="orange"); # 99% INFERIOR
abline(v=CuantilesA[8,2], lty=2, col="orange"); # 99% SUPERIOR
legend("topright",
legend=c("65%","70%","75%","80%","85%","90%","95%","99%"),
pch=c(1,2,3,4,5,6,7,8),
col=c("darkgoldenrod4","darkblue","aquamarine4",
"green", "brown","red","blue","orange"))
hist(tst, breaks = nbreaks, col = col_sequence,
main = 'Normalized Log2vsCDC51 pEhEx - ADJUSTED', lty=9)
abline(v=CuantilesD[1,1], lty=2, col="darkgoldenrod4"); # 65% INFERIOR
abline(v=CuantilesD[1,2], lty=2, col="darkgoldenrod4"); # 65% SUPERIOR
abline(v=CuantilesD[2,1], lty=2, col="darkblue"); # 70% INFERIOR
abline(v=CuantilesD[2,2], lty=2, col="darkblue"); # 70% SUPERIOR
abline(v=CuantilesD[3,1], lty=2, col="aquamarine4"); # 75% INFERIOR
abline(v=CuantilesD[3,2], lty=2, col="aquamarine4"); # 75% SUPERIOR
abline(v=CuantilesD[4,1], lty=2, col="green"); # 80% INFERIOR
abline(v=CuantilesD[4,2], lty=2, col="green"); # 80% SUPERIOR
abline(v=CuantilesD[5,1], lty=2, col="brown"); # 85% INFERIOR
abline(v=CuantilesD[5,2], lty=2, col="brown"); # 85% SUPERIOR
abline(v=CuantilesD[6,1], lty=2, col="red"); # 90% INFERIOR
abline(v=CuantilesD[6,2], lty=2, col="red"); # 90% SUPERIOR
abline(v=CuantilesD[7,1], lty=2, col="blue"); # 95% INFERIOR
abline(v=CuantilesD[7,2], lty=2, col="blue"); # 95% SUPERIOR
abline(v=CuantilesD[8,1], lty=2, col="orange"); # 99% INFERIOR
abline(v=CuantilesD[8,2], lty=2, col="orange"); # 99% SUPERIOR
legend("topright",
legend=c("65%","70%","75%","80%","85%","90%","95%","99%"),
pch=c(1,2,3,4,5,6,7,8),
col=c("darkgoldenrod4","darkblue","aquamarine4",
"green", "brown","red","blue","orange"))
Grafica Cuantiles del \(65\%\) y \(80\%\)
par(mfrow=c(2,1))
hist(tst, breaks = nbreaks, col = col_sequence,
main = 'Normalized Log2vsCDC51 pEhEx - DATA', lty=9)
abline(v=CuantilesA[1,1], lty=2, col="darkgoldenrod4"); # 65% INFERIOR
abline(v=CuantilesA[1,2], lty=2, col="darkgoldenrod4"); # 65% SUPERIOR
abline(v=CuantilesA[4,1], lty=2, col="green"); # 80% INFERIOR
abline(v=CuantilesA[4,2], lty=2, col="green"); # 80% SUPERIOR
legend("topright",legend=c("65%","80%"),
pch=c(1,2),col=c("darkgoldenrod4","green"))
hist(tst, breaks = nbreaks, col = col_sequence,
main = 'Normalized Log2vsCDC51 - ADJUSTED', lty=9)
abline(v=CuantilesD[1,1], lty=2, col="darkgoldenrod4"); # 65% INFERIOR
abline(v=CuantilesD[1,2], lty=2, col="darkgoldenrod4"); # 65% SUPERIOR
abline(v=CuantilesD[4,1], lty=2, col="green"); # 80% INFERIOR
abline(v=CuantilesD[4,2], lty=2, col="green"); # 80% SUPERIOR
legend("topright",legend=c("65%","80%"),
pch=c(1,2),col=c("darkgoldenrod4","green"))
Grafica Cuantiles del \(70\%\) y \(85\%\)
par(mfrow=c(2,1))
hist(tst, breaks = nbreaks, col = col_sequence,
main = 'Normalized Log2vsCDC51 pEhEx - DATA', lty=9)
abline(v=CuantilesA[2,1], lty=2, col="darkblue");
abline(v=CuantilesA[2,2], lty=2, col="darkblue")
abline(v=CuantilesA[5,1], lty=2, col="brown");
abline(v=CuantilesA[5,2], lty=2, col="brown")
legend("topright",legend=c("70%","85%"),
pch=c(1,2),#3,4,5,6,7,8),
col=c("brown"))
hist(tst, breaks = nbreaks, col = col_sequence,
main = 'Normalized Log2vsCDC51 pEhEx - ADJUSTED', lty=9)
abline(v=CuantilesD[2,1], lty=2, col="darkblue");
abline(v=CuantilesD[2,2], lty=2, col="darkblue")
abline(v=CuantilesD[5,1], lty=2, col="brown");
abline(v=CuantilesD[5,2], lty=2, col="brown")
legend("topright",legend=c("70%","85%"),
pch=c(1,2),#3,4,5,6,7,8),
col=c("brown"))
Muestra pEhExvsCDC52
log2vsCDC52 <- data4$log2samplevsCDC52; head(mean(log2vsCDC52)); head(sd(log2vsCDC52))## [1] 6.079551## [1] 2.958446head(log2vsCDC52,5)## [1] 6.157415 7.700407 9.768929 8.671526 6.790609Primer Histograma
ndata4 <- length(log2vsCDC52)
hist(log2vsCDC52, breaks = nbreaks, col= rainbow(25,0.3),
main = 'Log2vsCDC52')
meanlog2vsCDC52 <- mean(log2vsCDC52); head(meanlog2vsCDC52)## [1] 6.079551StdDevlog2vsCDC52 <- sd(log2vsCDC52); head(StdDevlog2vsCDC52)## [1] 2.958446Normlog2vsCDC52 <- (log2vsCDC52-meanlog2vsCDC52)/StdDevlog2vsCDC52; head(Normlog2vsCDC52)## [1] 0.02631943 0.54787417 1.24706626 0.87612726 0.24034856 -0.30491137tst<- Normlog2vsCDC52** Segundo Histograma**
hist(tst, breaks = nbreaks, col= 1:5,
main = 'Normalized Log2vsCDC52',
xlab='pEhEx1',
ylab= 'Frequency pEhEx')
Ajustando modelo
fw1<-fitdist(tst, "norm")
plotdist(tst, histo = TRUE, demp = TRUE)
nnorm.f <- fitdist(tst,"norm")
summary(nnorm.f)## Fitting of the distribution ' norm ' by maximum likelihood
## Parameters :
## estimate Std. Error
## mean -9.044811e-17 0.01451411
## sd 9.998946e-01 0.01026298
## Loglikelihood: -6733.782 AIC: 13471.56 BIC: 13484.49
## Correlation matrix:
## mean sd
## mean 1.000000e+00 -3.386913e-11
## sd -3.386913e-11 1.000000e+00par(mfrow=c(2,2))
denscomp(nnorm.f,legendtext = 'Dist Normal')
qqcomp(nnorm.f,legendtext = 'Dist Normal')
cdfcomp(nnorm.f,legendtext = 'Dist Normal')
ppcomp(nnorm.f,legendtext = 'Dist Normal')
Cálculo de cuantiles
probs <- c();
probs[8] = 0.175; probs[9] = 0.825;
probs[7] = 0.15; probs[10] = 0.85;
probs[6] = 0.125; probs[11] = 0.875;
probs[5] = 0.1; probs[12] = 0.9;
probs[4] = 0.075; probs[13] = 0.925;
probs[3] = 0.05; probs[14] = 0.95;
probs[2] = 0.025; probs[15] = 0.975;
probs[1] = 0.005; probs[16] = 0.995;
CuantilesData <- quantile(tst,prob = probs)
CuantilesModel <- qnorm(probs, mean=0, sd=1)
Cuantilillos <- t(CuantilesModel)
colnames(Cuantilillos) <- c('0.5%','2.5%','5%','7.5%',
'10%','12.5%','15%','17.5%',
'82.5%','85%','87.5%','90%',
'92.5%','95%','97.5%','99.5%')
Cuantilillos <- t(Cuantilillos)
colnames(Cuantilillos) <- c('Cuantiles Ajuste')
print(Cuantilillos)## Cuantiles Ajuste
## 0.5% -2.5758293
## 2.5% -1.9599640
## 5% -1.6448536
## 7.5% -1.4395315
## 10% -1.2815516
## 12.5% -1.1503494
## 15% -1.0364334
## 17.5% -0.9345893
## 82.5% 0.9345893
## 85% 1.0364334
## 87.5% 1.1503494
## 90% 1.2815516
## 92.5% 1.4395315
## 95% 1.6448536
## 97.5% 1.9599640
## 99.5% 2.5758293CuantilesA <- matrix(0,8,2)
CuantilesD <- matrix(0,8,2)
colnames(CuantilesA) <- c('LimInf','LimSup')
colnames(CuantilesD) <- c('LimInf','LimSup')
rownames(CuantilesA) <- c('65','70','75','80','85','90','95','99')
rownames(CuantilesD) <- c('65','70','75','80','85','90','95','99')
CuantilesA[1,1] <-CuantilesData[8]; CuantilesA[1,2] <-CuantilesData[9]
CuantilesA[2,1] <-CuantilesData[7]; CuantilesA[2,2] <-CuantilesData[10]
CuantilesA[3,1] <-CuantilesData[6]; CuantilesA[3,2] <-CuantilesData[11]
CuantilesA[4,1] <-CuantilesData[5]; CuantilesA[4,2] <-CuantilesData[12]
CuantilesA[5,1] <-CuantilesData[4]; CuantilesA[5,2] <-CuantilesData[13]
CuantilesA[6,1] <-CuantilesData[3]; CuantilesA[6,2] <-CuantilesData[14]
CuantilesA[7,1] <-CuantilesData[2]; CuantilesA[7,2] <-CuantilesData[15]
CuantilesA[8,1] <-CuantilesData[1]; CuantilesA[8,2] <-CuantilesData[16]
CuantilesD[1,1] <-Cuantilillos[8]; CuantilesD[1,2] <-Cuantilillos[9]
CuantilesD[2,1] <-Cuantilillos[7]; CuantilesD[2,2] <-Cuantilillos[10]
CuantilesD[3,1] <-Cuantilillos[6]; CuantilesD[3,2] <-Cuantilillos[11]
CuantilesD[4,1] <-Cuantilillos[5]; CuantilesD[4,2] <-Cuantilillos[12]
CuantilesD[5,1] <-Cuantilillos[4]; CuantilesD[5,2] <-Cuantilillos[13]
CuantilesD[6,1] <-Cuantilillos[3]; CuantilesD[6,2] <-Cuantilillos[14]
CuantilesD[7,1] <-Cuantilillos[2]; CuantilesD[7,2] <-Cuantilillos[15]
CuantilesD[8,1] <-Cuantilillos[1]; CuantilesD[8,2] <-Cuantilillos[16]
print(CuantilesD)## LimInf LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293print(CuantilesA)## LimInf LimSup
## 65 -0.8703761 0.8136491
## 70 -0.9170047 0.9590243
## 75 -0.9685679 1.1169792
## 80 -1.0262349 1.3717093
## 85 -1.0916475 1.6775570
## 90 -1.1672140 2.1543218
## 95 -1.3663234 2.6103392
## 99 -2.0549813 3.1594798Histogramas
col_sequence <- rainbow(n = 7, alpha = 0.35, start = 0, end = 1)
par(mfrow=c(2,1))
hist(tst, breaks = nbreaks, col = col_sequence,
main = 'Normalized Log2vsCDC52 pEhEx - DATA', lty = 9)
abline(v=CuantilesA[1,1], lty=2, col="darkgoldenrod4"); # 65% INFERIOR
abline(v=CuantilesA[1,2], lty=2, col="darkgoldenrod4"); # 65% SUPERIOR
abline(v=CuantilesA[2,1], lty=2, col="darkblue"); # 70% INFERIOR
abline(v=CuantilesA[2,2], lty=2, col="darkblue") # 70% SUPERIOR
abline(v=CuantilesA[3,1], lty=2, col="aquamarine4"); # 75% INFERIOR
abline(v=CuantilesA[3,2], lty=2, col="aquamarine4"); # 75% SUPERIOR
abline(v=CuantilesA[4,1], lty=2, col="green"); # 80% INFERIOR
abline(v=CuantilesA[4,2], lty=2, col="green"); # 80% SUPERIOR
abline(v=CuantilesA[5,1], lty=2, col="brown"); # 85% INFERIOR
abline(v=CuantilesA[5,2], lty=2, col="brown"); # 85% SUPERIOR
abline(v=CuantilesA[6,1], lty=2, col="red"); # 90% INFERIOR
abline(v=CuantilesA[6,2], lty=2, col="red"); # 90% SUPERIOR
abline(v=CuantilesA[7,1], lty=2, col="blue"); # 95% INFERIOR
abline(v=CuantilesA[7,2], lty=2, col="blue"); # 95% SUPERIOR
abline(v=CuantilesA[8,1], lty=2, col="orange"); # 99% INFERIOR
abline(v=CuantilesA[8,2], lty=2, col="orange"); # 99% SUPERIOR
legend("topright",
legend=c("65%","70%","75%","80%","85%","90%","95%","99%"),
pch=c(1,2,3,4,5,6,7,8),
col=c("darkgoldenrod4","darkblue","aquamarine4",
"green", "brown","red","blue","orange"))
hist(tst, breaks = nbreaks, col = col_sequence,
main = 'Normalized Log2vsCDC52 pEhEx - ADJUSTED', lty=9)
abline(v=CuantilesD[1,1], lty=2, col="darkgoldenrod4"); # 65% INFERIOR
abline(v=CuantilesD[1,2], lty=2, col="darkgoldenrod4"); # 65% SUPERIOR
abline(v=CuantilesD[2,1], lty=2, col="darkblue"); # 70% INFERIOR
abline(v=CuantilesD[2,2], lty=2, col="darkblue"); # 70% SUPERIOR
abline(v=CuantilesD[3,1], lty=2, col="aquamarine4"); # 75% INFERIOR
abline(v=CuantilesD[3,2], lty=2, col="aquamarine4"); # 75% SUPERIOR
abline(v=CuantilesD[4,1], lty=2, col="green"); # 80% INFERIOR
abline(v=CuantilesD[4,2], lty=2, col="green"); # 80% SUPERIOR
abline(v=CuantilesD[5,1], lty=2, col="brown"); # 85% INFERIOR
abline(v=CuantilesD[5,2], lty=2, col="brown"); # 85% SUPERIOR
abline(v=CuantilesD[6,1], lty=2, col="red"); # 90% INFERIOR
abline(v=CuantilesD[6,2], lty=2, col="red"); # 90% SUPERIOR
abline(v=CuantilesD[7,1], lty=2, col="blue"); # 95% INFERIOR
abline(v=CuantilesD[7,2], lty=2, col="blue"); # 95% SUPERIOR
abline(v=CuantilesD[8,1], lty=2, col="orange"); # 99% INFERIOR
abline(v=CuantilesD[8,2], lty=2, col="orange"); # 99% SUPERIOR
legend("topright",
legend=c("65%","70%","75%","80%","85%","90%","95%","99%"),
pch=c(1,2,3,4,5,6,7,8),
col=c("darkgoldenrod4","darkblue","aquamarine4",
"green", "brown","red","blue","orange"))
Grafica Cuantiles del \(65\%\) y \(80\%\)
par(mfrow=c(2,1))
hist(tst, breaks = nbreaks, col = col_sequence,
main = 'Normalized Log2vsCDC52 pEhEx - DATA', lty=9)
abline(v=CuantilesA[1,1], lty=2, col="darkgoldenrod4"); # 65% INFERIOR
abline(v=CuantilesA[1,2], lty=2, col="darkgoldenrod4"); # 65% SUPERIOR
abline(v=CuantilesA[4,1], lty=2, col="green"); # 80% INFERIOR
abline(v=CuantilesA[4,2], lty=2, col="green"); # 80% SUPERIOR
legend("topright",legend=c("65%","80%"),
pch=c(1,2),col=c("darkgoldenrod4","green"))
hist(tst, breaks = nbreaks, col = col_sequence,
main = 'Normalized Log2vsCDC52 - ADJUSTED', lty=9)
abline(v=CuantilesD[1,1], lty=2, col="darkgoldenrod4"); # 65% INFERIOR
abline(v=CuantilesD[1,2], lty=2, col="darkgoldenrod4"); # 65% SUPERIOR
abline(v=CuantilesD[4,1], lty=2, col="green"); # 80% INFERIOR
abline(v=CuantilesD[4,2], lty=2, col="green"); # 80% SUPERIOR
legend("topright",legend=c("65%","80%"),
pch=c(1,2),col=c("darkgoldenrod4","green"))
Grafica Cuantiles del \(70\%\) y \(85\%\)
par(mfrow=c(2,1))
hist(tst, breaks = nbreaks, col = col_sequence,
main = 'Normalized Log2vsCDC52 pEhEx - DATA', lty=9)
abline(v=CuantilesA[2,1], lty=2, col="darkblue");
abline(v=CuantilesA[2,2], lty=2, col="darkblue")
abline(v=CuantilesA[5,1], lty=2, col="brown");
abline(v=CuantilesA[5,2], lty=2, col="brown")
legend("topright",legend=c("70%","85%"),
pch=c(1,2),#3,4,5,6,7,8),
col=c("brown"))
hist(tst, breaks = nbreaks, col = col_sequence,
main = 'Normalized Log2vsCDC52 pEhEx - ADJUSTED', lty=9)
abline(v=CuantilesD[2,1], lty=2, col="darkblue");
abline(v=CuantilesD[2,2], lty=2, col="darkblue")
abline(v=CuantilesD[5,1], lty=2, col="brown");
abline(v=CuantilesD[5,2], lty=2, col="brown")
legend("topright",legend=c("70%","85%"),
pch=c(1,2),#3,4,5,6,7,8),
col=c("brown"))
Muestra pEhExvsCDC53
log2vsCDC53 <- data4$log2samplevsCDC53; head(mean(log2vsCDC53)); head(sd(log2vsCDC53))## [1] 6.151641## [1] 3.315575head(log2vsCDC53,5)## [1] 9.256488 6.538327 13.437122 10.651844 8.724550ndata4 <- length(log2vsCDC53)** Primer histograma**
hist(log2vsCDC53, breaks = nbreaks, col= rainbow(25,0.3),
main = 'Log2vsCDC53')
meanlog2vsCDC53 <- mean(log2vsCDC53); head(meanlog2vsCDC53)## [1] 6.151641StdDevlog2vsCDC53 <- sd(log2vsCDC53); head(StdDevlog2vsCDC53)## [1] 3.315575Normlog2vsCDC53 <- (log2vsCDC53-meanlog2vsCDC53)/StdDevlog2vsCDC53; head(Normlog2vsCDC53)## [1] 0.9364429 0.1166271 2.1973504 1.3572917 0.7760066 0.1877447tst<- Normlog2vsCDC53Segundo histograma
hist(tst, breaks = nbreaks, col= 1:5,
main = 'Normalized Log2vsCDC53',
xlab='pEhEx1',
ylab= 'Frequency pEhEx')
Ajustando modelo
fw1<-fitdist(tst, "norm")
plotdist(tst, histo = TRUE, demp = TRUE)
nnorm.f <- fitdist(tst,"norm")
summary(nnorm.f)## Fitting of the distribution ' norm ' by maximum likelihood
## Parameters :
## estimate Std. Error
## mean -1.080389e-16 0.01451411
## sd 9.998946e-01 0.01026298
## Loglikelihood: -6733.782 AIC: 13471.56 BIC: 13484.49
## Correlation matrix:
## mean sd
## mean 1 0
## sd 0 1par(mfrow=c(2,2))
denscomp(nnorm.f,legendtext = 'Dist Normal')
qqcomp(nnorm.f,legendtext = 'Dist Normal')
cdfcomp(nnorm.f,legendtext = 'Dist Normal')
ppcomp(nnorm.f,legendtext = 'Dist Normal')
Calculo de cuantiles
probs <- c();
probs[8] = 0.175; probs[9] = 0.825;
probs[7] = 0.15; probs[10] = 0.85;
probs[6] = 0.125; probs[11] = 0.875;
probs[5] = 0.1; probs[12] = 0.9;
probs[4] = 0.075; probs[13] = 0.925;
probs[3] = 0.05; probs[14] = 0.95;
probs[2] = 0.025; probs[15] = 0.975;
probs[1] = 0.005; probs[16] = 0.995;
CuantilesData <- quantile(tst,prob = probs)
CuantilesModel <- qnorm(probs, mean=0, sd=1)
Cuantilillos <- t(CuantilesModel)
colnames(Cuantilillos) <- c('0.5%','2.5%','5%','7.5%',
'10%','12.5%','15%','17.5%',
'82.5%','85%','87.5%','90%',
'92.5%','95%','97.5%','99.5%')
Cuantilillos <- t(Cuantilillos)
colnames(Cuantilillos) <- c('Cuantiles Ajuste')
print(Cuantilillos)## Cuantiles Ajuste
## 0.5% -2.5758293
## 2.5% -1.9599640
## 5% -1.6448536
## 7.5% -1.4395315
## 10% -1.2815516
## 12.5% -1.1503494
## 15% -1.0364334
## 17.5% -0.9345893
## 82.5% 0.9345893
## 85% 1.0364334
## 87.5% 1.1503494
## 90% 1.2815516
## 92.5% 1.4395315
## 95% 1.6448536
## 97.5% 1.9599640
## 99.5% 2.5758293CuantilesA <- matrix(0,8,2)
CuantilesD <- matrix(0,8,2)
colnames(CuantilesA) <- c('LimInf','LimSup')
colnames(CuantilesD) <- c('LimInf','LimSup')
rownames(CuantilesA) <- c('65','70','75','80','85','90','95','99')
rownames(CuantilesD) <- c('65','70','75','80','85','90','95','99')
CuantilesA[1,1] <-CuantilesData[8]; CuantilesA[1,2] <-CuantilesData[9]
CuantilesA[2,1] <-CuantilesData[7]; CuantilesA[2,2] <-CuantilesData[10]
CuantilesA[3,1] <-CuantilesData[6]; CuantilesA[3,2] <-CuantilesData[11]
CuantilesA[4,1] <-CuantilesData[5]; CuantilesA[4,2] <-CuantilesData[12]
CuantilesA[5,1] <-CuantilesData[4]; CuantilesA[5,2] <-CuantilesData[13]
CuantilesA[6,1] <-CuantilesData[3]; CuantilesA[6,2] <-CuantilesData[14]
CuantilesA[7,1] <-CuantilesData[2]; CuantilesA[7,2] <-CuantilesData[15]
CuantilesA[8,1] <-CuantilesData[1]; CuantilesA[8,2] <-CuantilesData[16]
CuantilesD[1,1] <-Cuantilillos[8]; CuantilesD[1,2] <-Cuantilillos[9]
CuantilesD[2,1] <-Cuantilillos[7]; CuantilesD[2,2] <-Cuantilillos[10]
CuantilesD[3,1] <-Cuantilillos[6]; CuantilesD[3,2] <-Cuantilillos[11]
CuantilesD[4,1] <-Cuantilillos[5]; CuantilesD[4,2] <-Cuantilillos[12]
CuantilesD[5,1] <-Cuantilillos[4]; CuantilesD[5,2] <-Cuantilillos[13]
CuantilesD[6,1] <-Cuantilillos[3]; CuantilesD[6,2] <-Cuantilillos[14]
CuantilesD[7,1] <-Cuantilillos[2]; CuantilesD[7,2] <-Cuantilillos[15]
CuantilesD[8,1] <-Cuantilillos[1]; CuantilesD[8,2] <-Cuantilillos[16]
print(CuantilesD)## LimInf LimSup
## 65 -0.9345893 0.9345893
## 70 -1.0364334 1.0364334
## 75 -1.1503494 1.1503494
## 80 -1.2815516 1.2815516
## 85 -1.4395315 1.4395315
## 90 -1.6448536 1.6448536
## 95 -1.9599640 1.9599640
## 99 -2.5758293 2.5758293print(CuantilesA)## LimInf LimSup
## 65 -0.8351247 0.8675257
## 70 -0.9466147 0.9977469
## 75 -0.9466147 1.1516663
## 80 -1.0968948 1.3476203
## 85 -1.3283399 1.5933747
## 90 -1.3283399 1.8884142
## 95 -1.8553769 2.3619041
## 99 -1.8553769 3.1601836Histogramas
col_sequence <- rainbow(n = 7, alpha = 0.35, start = 0, end = 1)
par(mfrow=c(2,1))
hist(tst, breaks = nbreaks, col = col_sequence,
main = 'Normalized Log2vsCDC53 pEhEx - DATA', lty = 9)
abline(v=CuantilesA[1,1], lty=2, col="darkgoldenrod4"); # 65% INFERIOR
abline(v=CuantilesA[1,2], lty=2, col="darkgoldenrod4"); # 65% SUPERIOR
abline(v=CuantilesA[2,1], lty=2, col="darkblue"); # 70% INFERIOR
abline(v=CuantilesA[2,2], lty=2, col="darkblue") # 70% SUPERIOR
abline(v=CuantilesA[3,1], lty=2, col="aquamarine4"); # 75% INFERIOR
abline(v=CuantilesA[3,2], lty=2, col="aquamarine4"); # 75% SUPERIOR
abline(v=CuantilesA[4,1], lty=2, col="green"); # 80% INFERIOR
abline(v=CuantilesA[4,2], lty=2, col="green"); # 80% SUPERIOR
abline(v=CuantilesA[5,1], lty=2, col="brown"); # 85% INFERIOR
abline(v=CuantilesA[5,2], lty=2, col="brown"); # 85% SUPERIOR
abline(v=CuantilesA[6,1], lty=2, col="red"); # 90% INFERIOR
abline(v=CuantilesA[6,2], lty=2, col="red"); # 90% SUPERIOR
abline(v=CuantilesA[7,1], lty=2, col="blue"); # 95% INFERIOR
abline(v=CuantilesA[7,2], lty=2, col="blue"); # 95% SUPERIOR
abline(v=CuantilesA[8,1], lty=2, col="orange"); # 99% INFERIOR
abline(v=CuantilesA[8,2], lty=2, col="orange"); # 99% SUPERIOR
legend("topright",
legend=c("65%","70%","75%","80%","85%","90%","95%","99%"),
pch=c(1,2,3,4,5,6,7,8),
col=c("darkgoldenrod4","darkblue","aquamarine4",
"green", "brown","red","blue","orange"))
hist(tst, breaks = nbreaks, col = col_sequence,
main = 'Normalized Log2vsCDC53 pEhEx - ADJUSTED', lty=9)
abline(v=CuantilesD[1,1], lty=2, col="darkgoldenrod4"); # 65% INFERIOR
abline(v=CuantilesD[1,2], lty=2, col="darkgoldenrod4"); # 65% SUPERIOR
abline(v=CuantilesD[2,1], lty=2, col="darkblue"); # 70% INFERIOR
abline(v=CuantilesD[2,2], lty=2, col="darkblue"); # 70% SUPERIOR
abline(v=CuantilesD[3,1], lty=2, col="aquamarine4"); # 75% INFERIOR
abline(v=CuantilesD[3,2], lty=2, col="aquamarine4"); # 75% SUPERIOR
abline(v=CuantilesD[4,1], lty=2, col="green"); # 80% INFERIOR
abline(v=CuantilesD[4,2], lty=2, col="green"); # 80% SUPERIOR
abline(v=CuantilesD[5,1], lty=2, col="brown"); # 85% INFERIOR
abline(v=CuantilesD[5,2], lty=2, col="brown"); # 85% SUPERIOR
abline(v=CuantilesD[6,1], lty=2, col="red"); # 90% INFERIOR
abline(v=CuantilesD[6,2], lty=2, col="red"); # 90% SUPERIOR
abline(v=CuantilesD[7,1], lty=2, col="blue"); # 95% INFERIOR
abline(v=CuantilesD[7,2], lty=2, col="blue"); # 95% SUPERIOR
abline(v=CuantilesD[8,1], lty=2, col="orange"); # 99% INFERIOR
abline(v=CuantilesD[8,2], lty=2, col="orange"); # 99% SUPERIOR
legend("topright",
legend=c("65%","70%","75%","80%","85%","90%","95%","99%"),
pch=c(1,2,3,4,5,6,7,8),
col=c("darkgoldenrod4","darkblue","aquamarine4",
"green", "brown","red","blue","orange"))
Grafica Cuantiles del \(65\%\) y \(80\%\)
par(mfrow=c(2,1))
hist(tst, breaks = nbreaks, col = col_sequence,
main = 'Normalized Log2vsCDC53 pEhEx - DATA', lty=9)
abline(v=CuantilesA[1,1], lty=2, col="darkgoldenrod4"); # 65% INFERIOR
abline(v=CuantilesA[1,2], lty=2, col="darkgoldenrod4"); # 65% SUPERIOR
abline(v=CuantilesA[4,1], lty=2, col="green"); # 80% INFERIOR
abline(v=CuantilesA[4,2], lty=2, col="green"); # 80% SUPERIOR
legend("topright",legend=c("65%","80%"),
pch=c(1,2),col=c("darkgoldenrod4","green"))
hist(tst, breaks = nbreaks, col = col_sequence,
main = 'Normalized Log2vsCDC51 - ADJUSTED', lty=9)
abline(v=CuantilesD[1,1], lty=2, col="darkgoldenrod4"); # 65% INFERIOR
abline(v=CuantilesD[1,2], lty=2, col="darkgoldenrod4"); # 65% SUPERIOR
abline(v=CuantilesD[4,1], lty=2, col="green"); # 80% INFERIOR
abline(v=CuantilesD[4,2], lty=2, col="green"); # 80% SUPERIOR
legend("topright",legend=c("65%","80%"),
pch=c(1,2),col=c("darkgoldenrod4","green"))
Grafica Cuantiles del \(70\%\) y \(85\%\)
par(mfrow=c(2,1))
hist(tst, breaks = nbreaks, col = col_sequence,
main = 'Normalized Log2vsCDC53 pEhEx - DATA', lty=9)
abline(v=CuantilesA[2,1], lty=2, col="darkblue");
abline(v=CuantilesA[2,2], lty=2, col="darkblue")
abline(v=CuantilesA[5,1], lty=2, col="brown");
abline(v=CuantilesA[5,2], lty=2, col="brown")
legend("topright",legend=c("70%","85%"),
pch=c(1,2),#3,4,5,6,7,8),
col=c("brown"))
hist(tst, breaks = nbreaks, col = col_sequence,
main = 'Normalized Log2vsCDC53 pEhEx - ADJUSTED', lty=9)
abline(v=CuantilesD[2,1], lty=2, col="darkblue");
abline(v=CuantilesD[2,2], lty=2, col="darkblue")
abline(v=CuantilesD[5,1], lty=2, col="brown");
abline(v=CuantilesD[5,2], lty=2, col="brown")
legend("topright",legend=c("70%","85%"),
pch=c(1,2),#3,4,5,6,7,8),
col=c("brown"))